Comments (3)
Dear Ingrid,
I come to this repo quite frequently, but your message went under the radar somehow. I am sorry for that.
I am going to check your code and write you ASAP.
Blas
from spatialrf.
Ok, I now see what happened here.
If you run your spatial model with "verbose = TRUE" you will see the message "The model residuals are not spatially correlated, there is no need to fit a spatial model", meaning that you don't need a spatial model for your data because the residuals are not spatially autocorrelated.
You can check this in the "model.non.spatial" by plotting the Moran's I of the residuals using "plot_moran(model.non.spatial)".
Please, let me know when you have read this message so I can close this non-issue.
I appreciate your interest in the package!
Blas
from spatialrf.
from spatialrf.
Related Issues (18)
- interoperability with {randomForestExplainer} by changing `my_forest$call` to be more {ranger}-like? HOT 4
- `$evaluation` results from `rf_evaluate()` sensitive to order of character vector passed to `metrics=` argument HOT 2
- use of `case.weights` versus `class.weights` in the case of a binary response?
- Does "negative spatial correlation" need fitting a spatial model? HOT 3
- Predictions for the test data
- How to compute distance.matrix for the spatialRF::rf_spatial function
- Apply spatialRF model parameters to unseen data at finer spatial scale HOT 4
- Some questions about the definition of the distance matrix HOT 2
- interaction_explorer error: creation of NAs in the transposition step
- Is there a function in the package to get the distance matrix HOT 1
- Prediction function for spatial models? HOT 6
- Issues with distance matrix
- Partial dependence plots not working properly
- having the `pkgdown` website URL on the repo page HOT 2
- Spatial prediction using SpatialRF over large area HOT 2
- prediction for the test data
- the_feature_engineer() returns different data types depending on whether promising interactions are found HOT 3
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